Asymmetric Generalized Gaussian Mixture Models and EM algorithm for Image Segmentation

Abstract : In this paper, a parametric and unsupervised histogram-based image segmentation method is presented. The histogram is assumed to be a mixture of asymmetric generalized Gaussian distributions. The mixture parameters are estimated by using the Expectation Maximization algorithm. Histogram fitting and region uniformity measures on synthetic and real images reveal the effectiveness of the proposed model compared to the generalized Gaussian mixture model.
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Communication dans un congrès
20th International Conference on Pattern Recognition - ICPR 2010, Aug 2010, Istanbul, Turkey. IEEE Computer Society, pp.4557 - 4560, 2010, ICPR 2010 - 20th International Conference on Pattern Recognition. 〈10.1109/ICPR.2010.1107〉
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https://hal.inria.fr/inria-00542496
Contributeur : Salvatore Tabbone <>
Soumis le : jeudi 2 décembre 2010 - 17:12:00
Dernière modification le : jeudi 11 janvier 2018 - 06:23:16

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Nafaa Nacereddine, Salvatore Tabbone, Djemel Ziou, Latifa Hamami. Asymmetric Generalized Gaussian Mixture Models and EM algorithm for Image Segmentation. 20th International Conference on Pattern Recognition - ICPR 2010, Aug 2010, Istanbul, Turkey. IEEE Computer Society, pp.4557 - 4560, 2010, ICPR 2010 - 20th International Conference on Pattern Recognition. 〈10.1109/ICPR.2010.1107〉. 〈inria-00542496〉

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